#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Author:Lijiacai
Email:1050518702@qq.com
===========================================
CopyRight@JackLee.com
===========================================
"""
from keras.layers import Conv2D, Input, MaxPool2D, Reshape, Activation, Flatten, Dense
from keras.models import Model, Sequential
from keras.layers.advanced_activations import PReLU
from keras.optimizers import adam
import numpy as np

import cv2


def getModel():
    input = Input(shape=[16, 66, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
    x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
    x = Activation("relu", name='relu1')(x)
    x = MaxPool2D(pool_size=2)(x)
    x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x)
    x = Activation("relu", name='relu2')(x)
    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
    x = Activation("relu", name='relu3')(x)
    x = Flatten()(x)
    output = Dense(2, name="dense")(x)
    output = Activation("relu", name='relu4')(output)
    model = Model([input], [output])
    return model


model = getModel()
model.load_weights("./model/model12.h5")


def getmodel():
    return model


def gettest_model():
    input = Input(shape=[16, 66, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
    A = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
    B = Activation("relu", name='relu1')(A)
    C = MaxPool2D(pool_size=2)(B)
    x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(C)
    x = Activation("relu", name='relu2')(x)
    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
    K = Activation("relu", name='relu3')(x)

    x = Flatten()(K)
    dense = Dense(2, name="dense")(x)
    output = Activation("relu", name='relu4')(dense)
    x = Model([input], [output])
    x.load_weights("./model/model12.h5")
    ok = Model([input], [dense])

    for layer in ok.layers:
        print(layer)

    return ok


import tensorflow as tf

graph = tf.get_default_graph()


def finemappingVertical(image):
    resized = cv2.resize(image, (66, 16))
    resized = resized.astype(np.float) / 255
    with graph.as_default():
        res = model.predict(np.array([resized]))[0]
    # print("keras_predict",res)
    res = res * image.shape[1]
    res = res.astype(np.int)
    H, T = res
    H -= 3
    # 3 79.86
    # 4 79.3
    # 5 79.5
    # 6 78.3

    # T
    # T+1 80.9
    # T+2 81.75
    # T+3 81.75

    if H < 0:
        H = 0
    T += 2

    if T >= image.shape[1] - 1:
        T = image.shape[1] - 1

    image = image[0:35, H:T + 2]

    image = cv2.resize(image, (int(136), int(36)))
    return image
